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Real-Time Estimation of Satellite-Derived PM 2.5 Based on a Semi-Physical Geographically Weighted Regression Model

Author

Listed:
  • Tianhao Zhang

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Gang Liu

    (Shanghai Institute of Satellite Engineering, Shanghai 201100, China)

  • Zhongmin Zhu

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    College of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China)

  • Wei Gong

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China)

  • Yuxi Ji

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Yusi Huang

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

Abstract

The real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM 2.5 ) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM 2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM 2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM 2.5 pollution during winter. Seasonal average mass concentrations of PM 2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has been discussed. In summary, real-time PM 2.5 was estimated effectively by the satellite-based semi-physical GWR model, and the results could provide reasonable references for assessing health impacts and offer guidance on air quality management in China.

Suggested Citation

  • Tianhao Zhang & Gang Liu & Zhongmin Zhu & Wei Gong & Yuxi Ji & Yusi Huang, 2016. "Real-Time Estimation of Satellite-Derived PM 2.5 Based on a Semi-Physical Geographically Weighted Regression Model," IJERPH, MDPI, vol. 13(10), pages 1-13, September.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:10:p:974-:d:79686
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    References listed on IDEAS

    as
    1. Tianhao Zhang & Zhongmin Zhu & Wei Gong & Hao Xiang & Ruimin Fang, 2016. "Characteristics of Fine Particles in an Urban Atmosphere—Relationships with Meteorological Parameters and Trace Gases," IJERPH, MDPI, vol. 13(8), pages 1-16, August.
    2. Yoram J. Kaufman & Didier Tanré & Olivier Boucher, 2002. "A satellite view of aerosols in the climate system," Nature, Nature, vol. 419(6903), pages 215-223, September.
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    Cited by:

    1. Jamal Jokar Arsanjani, 2017. "Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial," IJERPH, MDPI, vol. 14(4), pages 1-3, April.
    2. Tianhao Zhang & Wei Gong & Wei Wang & Yuxi Ji & Zhongmin Zhu & Yusi Huang, 2016. "Ground Level PM 2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO 2 and Enhanced Vegetation Index (EVI)," IJERPH, MDPI, vol. 13(12), pages 1-12, December.
    3. Yang Li & Jun Tao & Leiming Zhang & Xiaofang Jia & Yunfei Wu, 2016. "High Contributions of Secondary Inorganic Aerosols to PM 2.5 under Polluted Levels at a Regional Station in Northern China," IJERPH, MDPI, vol. 13(12), pages 1-15, December.
    4. Hujia Zhao & Huizheng Che & Yanjun Ma & Yangfeng Wang & Hongbin Yang & Yuche Liu & Yaqiang Wang & Hong Wang & Xiaoye Zhang, 2017. "The Relationship of PM Variation with Visibility and Mixing-Layer Height under Hazy/Foggy Conditions in the Multi-Cities of Northeast China," IJERPH, MDPI, vol. 14(5), pages 1-18, April.

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